metabrainz/acousticbrainz-server


The server components for the AcousticBrainz project

https://acousticbrainz.org/

License: GPL-2.0

Language: Python

Keywords: acousticbrainz-server, big-data, machine-learning, music, python, web


acousticbrainz-server

The server components for the new AcousticBrainz project.

Please report issues here: http://tickets.musicbrainz.org/browse/AB

Installation

Vagrant VM

The easiest way to start is to setup ready-to-use Vagrant VM. To do that download and install Vagrant for your OS. Then copy two config files:

  1. config.py.sample to config.py (you don't need to modify this file)
  2. profile.conf.in.sample to profile.conf.in in the ./hl_extractor/ directory (in this file you need to set models_essentia_git_sha value)

After that you can spin up the VM and start working with it:

$ vagrant up
$ vagrant ssh

There are some environment variables that you can set to affect the provisioning of the virtual machine.

  • AB_NCPUS: Number of CPUs to put in the VM (default 1, 2 makes compilation faster)
  • AB_MEM: Amount of memory (default 1024mb)
  • AB_MIRROR: ubuntu mirror (default archive.ubuntu.com)
  • AB_NOHL: If set, don't compile the highlevel calculation tools (not needed for regular server development)

There are some shortcuts defined using fabric to perform commonly used commands:

  • fab vpsql: Load a psql session. Requires a local psql client
  • fab vssh: Connect to the VM more efficiently, saving the settings so that you don't need to run vagrant each time you ssh.

Manually

Full installation instructions are available in INSTALL.md file. After installing, continue the following steps.

Configuration and development

Building static files

We use Gulp as our JavaScript/CSS build system. node.js dependencies. Calling gulp on its own will build everything necessary to access the server in a web browser:

./node_modules/.bin/gulp

Keep in mind that you'll need to rebuild static files after you modify JavaScript or CSS.

Login

To use the dataset tools you need to configure OAuth with MusicBrainz. Log in to your MusicBrainz account (or create one if needed) and create a new application.

Choose a name (for example, "AcousticBrainz development"), set Type to "Web Application" and set the Callback URL to http://localhost:8080/login/musicbrainz/post

Copy the OAuth Client ID and OAuth Client Secret values to config.py as MUSICBRAINZ_CLIENT_ID and MUSICBRAINZ_CLIENT_SECRET.

You should now be able to use the menu in the top corner of your AcousticBrainz server to log in.

Admin interface

Once you have logged in, you can make your user an admin, by running

python manage.py add_admin <your user>

You should now be able to access the admin section at http://localhost:8080/admin

Running

Before starting the server you will need to build static files:

$ cd acousticbrainz-server
$ fab build_static

Keep in mind that you'll need to rebuild static files after you modify JavaScript or CSS.

You can start the web server (will be available at http://localhost:8080/):

$ cd acousticbrainz-server
$ python manage.py runserver

the high-level data extractor:

$ cd acousticbrainz-server/hl_extractor
$ python hl_calc.py

the dataset evaluator:

$ cd acousticbrainz-server/dataset_eval
$ python evaluate.py

Working with data

Importing

AcousticBrainz provides data dumps that you can import into your own server. Latest database dump is available at http://acousticbrainz.org/download. You need to download full database dump from this page and use it during database initialization:

$ python manage.py init_db path_to_the_archive

you can also easily remove existing database before initialization using --force option:

$ python manage.py init_db --force path_to_the_archive

or import archive after database is created:

$ python manage.py import_data path_to_the_archive

You can also import dumps that you created yourself. This process is described below (see dump full_db command).

Exporting

There are several ways to export data out of AcousticBrainz server. You can create full database dump or export only low-level and high-level data in JSON format. Both ways support incremental dumping.

Examples

Full database dump:

$ python manage.py dump full_db

JSON dump:

$ python manage.py dump json

Creates two separate full JSON dumps with low-level and high-level data.

Incremental dumps:

$ python manage.py dump incremental

Creates new incremental dump in three different formats: usual database dump, low-level and high-level JSON.

Previous incremental dumps:

$ python manage.py dump incremental --id 42

Same as another one, but recreates previously created incremental dump.

Project Statistics

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Roman Tsukanov Alastair Porter Robert Kaye Ian McEwen Swapnil Gupta Frederik “Freso” S. Olesen kartikgupta0909 Dmitry Bogdanov vibhorbajpai Saiful B. Khan JesseWeinstein Wieland Hoffmann Shadab Zafar Laurent Monin Caroline Gschwend Daniele Scarano Johannes Dewender Nupur Baghel

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